With the proliferation of image capture devices in the form of cameras, mobile phones with imaging capability, and video cameras with stills capability for example, and the relatively cheap nature of data storage for such devices, there has been an explosion of digital image data. An average user may have in the region of several thousand images stored on one or more of their computers, or stored in remote storage locations for access anywhere, anytime. A library of digital images can contain a large number of images in which one or more people are the subject or are otherwise in the image. This is particularly the case with consumer image collections, which will predominantly comprise images of events such as parties, weddings and the like. Clearly, with the vast number of digital images being captured, stored and consumed, it is important for users to be able to quickly and efficiently sort and categorise images into manageable collections in order to improve their experience when consuming, distributing or printing images.
There are methods which can be used to sort and categorise images in an image collection or library. For example, image management systems can analyze an image library and, with some user input, identify and categorize images on the basis of people identified within images of the library, or more specifically, on the basis of identified faces of people. As such, existing personal image management approaches generally rely on the use of automated face detectors which are used to find the people of interest in the images, and which rely on some user input in order to validate or otherwise augment the provision of face detection.
Such face detectors are not perfectly accurate, and they can regularly fail to find a person, especially when the face of the person is partially or completely undetectable to the detector such as, for example, when the face is side-viewed, back-viewed or otherwise occluded by other people, objects or due to poor image capture parameters such that the face cannot accurately be identified and processed as required. Accordingly, a relatively low and generally unacceptable recall rate for such face detectors can limit the possibility of automatically retrieving all the images in a library or catalog containing each person of interest.